Iet Science Measurement & Technology最新文献

An adaptive Dynamic Surface Controller (DSC) is designed for a two-axis gimbal system with actuator dynamics in the presence of parametric uncertainties in [1]. A Lyapunov stability analysis is used to guarantee the convergence of the tracking error to the origin and boundedness of all closed-loop signals. The main objective of this corrigendum is to point out several errors that occurred throughout the paper, resulting in the inaccuracy of the used dynamic model and ineffectiveness of the proposed controller. It should be noted that taking into account the corrections stated in this corrigendum, the main result of the original paper is still valid.

Rectal cancer is one of the most common lower gastrointestinal diseases worldwide. Currently, the common treatment is low anterior resection (LAR) of the rectum, which preserves the anus of the patient. However, it is easy to cause low anterior resection syndrome after surgery, which has a significant negative impact on the life of patients, and there is no unified evaluation standard for postoperative rectal function. To solve this problem, a multi-sensor fusion rectal information acquisition system is designed in this paper, and a rectal signal processing method is proposed to theoretically evaluate the rectal function of postoperative patients. The method uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the one-dimensional rectal signal to solve the underdetermined ICA problem, uses the Fast independent component analysis (Fast-ICA) to separate the pure rectal signal, uses the wavelet packet to extract features, and uses the particle swarm optimization optimizes support vector machine (PSO-SVM) to classify and evaluate postoperative function. According to the experimental results, the rectal signal preprocessing effect is good, the evaluation prediction rate is 99.5565%, and the algorithm classification results are accurate, which provides a certain preliminary theoretical basis and reference value for the evaluation of rectal function after LAR.

The unmanned aerial vehicle (UAV) and the unmanned ground vehicle (UGV) can complete complex tasks through information sharing and ensure the mission execution capability of multiple unmanned carrier platforms. At the same time, cooperative navigation can use the information interaction between multi-platform sensors to improve the relative navigation and positioning accuracy of the entire system. Aiming at the problem of deviation of the system model due to gross errors in sensor measurement data or strong manoeuvrability in complex environments, a robust and adaptive UGV-UAV relative navigation and positioning algorithm is proposed. In this paper, the relative navigation and positioning of UGV-UAV is studied based on inertial measurement unit (IMU)/Vision. Based on analyzing the relative kinematics model and sensor measurement model, a leader (UGV)-follow (UAV) relative navigation model is established. In the implementation of the relative navigation and positioning algorithm, the robust adaptive algorithm and the non-linear Kalman filter (extended Kalman filter [EKF]) algorithm are combined to dynamically adjust the system state parameters. Finally, a mathematical simulation of the accompanying and landing process in the UGV-UAV cooperative scene is carried out. The relative position, velocity and attitude errors calculated by EKF, Robust-EKF and Robust-Adaptive-EKF algorithms are compared and analyzed by simulating two cases where the noise obeys the Gaussian distribution and the non-Gaussian distribution. The results show that under the non-Gaussian distribution conditions, the accuracy of the Robust-Adaptive-EKF algorithm is improved by about two to three times compared with the EKF and Robust-EKF and can almost reach the relative navigation accuracy under the Gaussian distribution conditions. The robust self-adaptive relative navigation and positioning algorithm proposed in this paper has strong adaptability to the uncertainty and deviation of system modelling in complex environments, with higher accuracy and stronger robustness.

Partial discharge (PD) detection is used to evaluate the insulation status of high-voltage equipment. The most challenging aspect of traditional PD recognition is extracting features from the discharge signal. Accordingly, this study applied the visual geometry group-19 (VGG-19) model to gas-insulated switchgear (GIS) PD image recognition. A high frequency current transformer and an LDP-5 inductive sensor measured PD electrical signals emitted by 15-kV GIS. Next, the Hilbert energy spectrum was obtained by Hilbert transform in the time and frequency domains. Compared with a phase-resolved PD pattern, the Hilbert spectrum can represent the energy and instantaneous frequency with the time variable. Finally, the VGG-19 model was applied for PD pattern recognition. For validation, its recognition performance was compared with that of a fractal theory by using a neural network method. The VGG-19 method is straightforward and has a high PD recognition rate, thereby enabling equipment manufacturers to quickly verify the insulation of GIS during assembly or operation.

There are many defects in gas-insulated metal-enclosed switchgear (GIS) and gas-insulated transmission lines (GIL) that may cause accidents, such as floating potentials, metal particles, air gaps, and cracks in support insulators. Nevertheless, to-this-date, there is no effective method to recognize them to adjust their operation status. Accordingly, this study compared three typical defect models to explore the partial discharge process, and three typical experimental models were established to identify the characteristic parameters of acoustic and electrical information in the development of defects. The research study shows that that as the voltage increases, the partial discharge of free-metal-particle defects and air-gap defects obtained by the pulse-current method increase gradually. A sudden increase also occurred in the partial discharge of the floating potential defect. The discharge amount of free conductive particles obtained by an ultrahigh-frequency detection method was distributed symmetrically on both sides of the applied voltage peak, and the other two defects were on one side of the peak and yielded an obvious phase difference. The particle collision signal obtained by the ultrasonic method was obvious, but had no obvious phase relationship with the applied voltage. However, the obtained floating potential defect information had obvious phase differences, and the ultrasonic method was not sensitive to air-gap defects. The three typical defects can be identified by the combined method more accurately; this provides a theoretical basis and data support for GIS and GIS voltage tests, detection technologies, and online monitoring methods.

Light spectrum analysis of leaves plays an essential role in measuring the contents of carbohydrates and other important compound like proteins. And it is also useful in evaluating the status of vegetation by remote sensing. Here, an approximation method considering the optical response characteristics of multispectral silicon (Si) sensor for the reflection or absorption spectra of leaves is proposed. The light spectrum is analysed by Gaussian mixture models (GMM) with fixed bandwidth firstly, and then the optimal model parameters are derived and validated to balance the complexity of Si sensor and the 2-norm deviation of spectra, and next a low-cost framework for the leaf spectroscopy with wireless node is illustrated according to the specific centre-wavelengths and bandwidths of the GMM, and finally, an experimental prototype with 18-channel Si sensor node and the developed mobile APP is demonstrated. The simplified strategy and its realization for leaf spectroscopy cast light on large-scale applications in future agriculture.

The stresses existing in power transformers lead to the generation of bubbles. They are the primary source that worsens and leads to insulation failure. Due to the complexity of bubble dynamics, it is challenging to figure out bubble mechanisms. However, creation, migration, and accumulation mechanisms require further explanation. A set of experiments has been conducted to provide a scientific overview of bubbles. The results revealed that buoyancy is dominant in low electric fields, while high electric fields affect bubbles' rise. Bubbles stretch and oscillate while moving. When the electric field is not strong, the AC field reduces the duration and volume of bubble detachment. Vibration stress on the other side is leading the bubble motion including detachment frequency and migration speed. Under the combination of AC and vibration stresses, the bubble distortion degree aggravates and bubble gathering formation is easy. This research can provide a deeper acumen of the bubble's actions beneath a power transformer under a combination of vibration and electric fields. It provides essential technique parameters to deal with while investigating bubble's problems and related engineering research areas.

Partial discharge (PD) detection is essential in assessing the insulation state of electrical equipment. However, PD signals are often overwhelmed by interference, resulting in inaccurate detection results. Aiming at this problem, this study proposes a PD detection method based on singular value decomposition (SVD) and improved spectral subtraction. First, the test signal is constructed as a Hankel matrix, which is used as a trajectory matrix for the SVD. Next, the singular value mutation point in the feature matrix is set as the threshold for removing the narrowband interference (NBI), and a signal containing only white noise is obtained. Finally, the improved spectral subtraction is used to remove white noise and improve the signal-to-noise ratio (SNR). The method proposed herein, along with the variational mode decomposition, the empirical mode decomposition, and the improved threshold wavelet method, are applied to the processing of PD signals. Also, the SNR value, waveform similarity coefficient, and mean square error of the denoizing signal of the four algorithms were calculated, considering the noise suppression and feature preservation abilities. The simulation and measurement results show that the SVD-spectral subtraction method has a strong suppression effect on narrow-band interference and white noise. Compared with other algorithms, this method can significantly improve the execution efficiency and has great application prospects.

The high-quality 3D model is the key to digital twin and visualization. Here, the neural radiation field (NeRF) is applied to greatly improve the speed and accuracy of 3D reconstruction of large-scale substation equipment. The incremental SFM algorithm is used to accurately position the camera in open scenes, and the frame extraction experiment indicates that the single angle increment should not be greater than 8°. The reconstruction test is carried out on the power transformer and high-voltage experimental equipment following the circular shooting track for instant-NGP (a variant of NeRF). Compared with the traditional 3D reconstruction method, the speed of reconstruction is accelerated by 23–55 times, and the fidelity is improved by 2–3 times. Also, the deployment scheme of NeRF in the digital twin technology of large-scale substation equipment is proposed. The experimental results verify the effectiveness and feasibility of NeRF in practical application.

A simulation model composed of an interfering cable and a disturbed cable is established for studying the lightning inductive characteristics between parallel cables. Effects of the horizontal distance between cables (5–40 cm), their height difference (−6–32 cm), as well as the length of disturbed cable (1–100 m) are investigated for the cable induced voltage, which is measured at the resistive load connected between one terminal of the disturbed cable and the ground, when various lightning surge currents (8/20 μs, 5/320 μs, and 0.5 μs/100 kHz) are delivered into the interfering cable. Our findings show that an increase of horizontal distance between cables can give rise to an exponential decrease of the cable induced voltage, and that there exist linear correlations between either the height difference of two cables or the length of disturbed cable and the cable induced voltage, which reaches maximum when the cables are located at the same height and of equal length. Verification experiments for the effect of horizontal distance have also been made, and, in general, they all show a reasonable agreement with the simulations. A detailed theoretical explanation of obtained findings is provided as well.